STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Efficient coordination and transmission of data for cooperative vehicular safety applications
Proceedings of the 3rd international workshop on Vehicular ad hoc networks
Vehicular Networks: Techniques, Standards, and Applications
Vehicular Networks: Techniques, Standards, and Applications
Traffic-Known Urban Vehicular Route Prediction Based on Partial Mobility Patterns
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Target Association Rules: A New Behavioral Patterns for Point of Coverage Wireless Sensor Networks
IEEE Transactions on Computers
Spatial and Temporal Analysis of Planet Scale Vehicular Imagery Data
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
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Vehicular ad-hoc networks (VANETs) are key players in the future self-organizing traffic information systems. Such systems provide safe environments on roads as they notify vehicles about congestions, accidents, and dangerous situations. VANETs facilitate broadcasting and geocasting notification messages among vehicles for the purpose of distributing important information among them. Mining and querying VANET is a key challenging task due to the spatial nature and huge amount of the information collected from the VANET's field. This paper adopts a modified version of STING (STatistical INformation Grid [1]) for building a grid-based spatial structure for storing the data collected from VANETs. The modified structure will allow an efficient querying and mining for interesting knowledge and patterns regarding vehicles and road conditions, as of the proposed framework implements a third dimension to represent time.